The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes

Trends Cogn Sci. 2019 Jul;23(7):584-601. doi: 10.1016/j.tics.2019.03.009. Epub 2019 May 29.


The imprecise nature of psychiatric nosology restricts progress towards characterizing and treating mental health disorders. One issue is the 'heterogeneity problem': different causal mechanisms may relate to the same disorder, and multiple outcomes of interest can occur within one individual. Our review tackles this heterogeneity problem, providing considerations, concepts, and approaches for investigators examining human cognition and mental health. We highlight the difficulty of pure dimensional approaches due to 'the curse of dimensionality'. Computationally, we consider supervised and unsupervised statistical approaches to identify putative subtypes within a population. However, we emphasize that subtype identification should be linked to a particular outcome or question. We conclude with novel hybrid approaches that can identify subtypes tied to outcomes, and may help advance precision diagnostic and treatment tools.

Keywords: functional random forest; heterogeneity; machine learning; mental health; surrogate variable analysis.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Brain / physiopathology
  • Cognition
  • Humans
  • Individuality
  • Mental Disorders / classification*
  • Mental Disorders / diagnosis
  • Mental Disorders / physiopathology
  • Mental Disorders / psychology
  • Models, Statistical